1. Chapter 25: Robotics April 27, 2004

2. The Week Ahead … Wednesday: Dmitrii Zagorodnov Thursday: Jeff Elser’s presentation, general discussion Friday: Rafal Angryk Monday: CS 536 final @ 2 p.m.

3. 25.1 Introduction Robot Components: –Sensors –Effectors –Processors Robot Types: –Manipulators (> 1 million worldwide) –Mobile (ULV and planetary, UAV, AUV) –Hybrid –Other (prosthetic devices, multibody systems)

4. Typical Environments Partially Observable Stochastic Dynamic Continuous

5. 25.2 Robot Hardware Sensors –passive (e.g. camera) –active (e.g. sonar, laser, radar) Record distances, Figure 25.2 Record images Record properties of robot (propriocaptive), e.g. inertial sensors

6. Effectors Degrees of Freedom (DOF), e.g. a wrist has 3 DOF A car has 2 controllable DOF but 3 effective DOF A non-holonomic robot has a higher effective DOF than controllable DOF

7. Effectors Most robot arms are holonomic (simpler) Most mobile robots are non-holonomic Prismatic joints allow sliding motion Revolute joints allow rotational motion Dynamic stability vs. Static stability Power Sources: electric motor, pneumatic actuator, hydraulic actuator

8. 25.7 Robotic Software Architecture Subsumption Architecture, Rodney Brooks, 1986 –Application: wall following –a framework to assemble reactive (as opposed to deliberative) controllers out of FSAs. –Figure 25.22 –Difficult to understand –Difficult to change behavior (wasp)

9. Three Layer Architecture Very common today Reactive Layer (sense-act loop) Executive Layer Deliberative Layer

10. Robotic Programming Languages General Robot Language, GRL, 2000 –function –uses FSMs as building blocks –provides communication and control constructs C++ Embedded Systems, CES, 2000 –integrates probability and learning

11. Robotic Programming Languages Reactive Action Plan System, RAPS, 1994 –can specify goals, plans, conditions for likely plan success ALisp, 2002 –can program non-deterministic choice points –learns via reinforcement learning

12. 25.8 Application Domains Industry Agriculture Transportation, Figure 25.23, the challenge is to use natural cues to locate robot Hazardous Environments Exploration, Figure 25.24 Health Care, Figure 25.23 Personal Service Entertainment, Figure 25.4b Human Augmentation

13. 25.4 Planning to Move Assume –motions are deterministic –localization is exact Point to point motion Compliant motion Configuration space includes location, orientation, joint angles

14. Path Planning Involves continuous spaces Two common techniques that map the continuous space onto a discrete space –cell decomposition –skeletonization

15. Configuration Space A workspace representation is easier. For example, in Figure 25.12(a) everything can be specified by (x e, y e ) and (x s, y s ) –The problem is that not all points are realizable

16. Configuration Space Use ( e, s ), the angles of the joints Kinematics: Maps a configuration space onto a workspace (easy) Inverse Kinematics: Maps a workspace onto a configuration space Obstacles, Figure 25.12b Free Space vs. Occupied Space, Figure 25.13

17. Cell Decomposition Figure 25.14 Each region can be solved simply Rectangles –hard for high dimensions –mixed cells are challenging (don’t want unsound solutions or incomplete problem solving ability) Irregular Shapes Potential Field, Figure 25.15

18. Skeletonization Reduce the robot’s free space to 1-D Voronoi Graphs, Figure 25.16a –Map the initial point onto the Voronoi Graph –Follow Voronoi Graph –Map point on Voronoi Graph onto goal point Probabilistic Roadmaps, Figure 25.16b –Offers more routes than Voronoi Graphs

19. Exercise 25.8 Humans are so adept at basic tasks such as picking up cups or stacking blocks that they often forget how complex these tasks are. In this exercise, you will discover the complexity and recapitulate the last 30 years of developments in robotics. First, pick a task, such as building an arch out of three blocks. Then, build a robot out of four humans as follows:

20. Exercise 25.8 Brain. The job of the Brain is to come up with a plan to achieve the goal and to direct the hands in the execution of the plan. The Brain receives input from the Eyes, but cannot see the scene directly. The brain is the only one who knows what the goals is. Eyes. The Eyes’ job is to report a brief description of the scene to the Brain. The Eyes should stand a few feet away from the working environment, and can provide qualitative descriptions or quantitative descriptions. Eyes can also answer questions from the Brain.

21. Exercise 25.8 Left Hand and Right Hand. One person plays each Hand. The two Hands stand next to each other; the Left Hand uses only his or her left hand, and the Right Hand only his or her right hand. The Hands execute only simple commands from the Brain – for example, “Left Hand, move two inches forward.” They cannot execute commands other than motions; for example, “Pick up the box” is not something a Hand can do. The Hands must be blindfolded. The only sensory capability they have is the ability to tell when their path is blocked by an immovable obstacle such as a table or the other Hand. In such cases, they can beep to inform the Brain of the difficulty.